When people consider AI’s eventual involvement in our lives, they tend to see a sophisticated system with sophisticated functions in a sophisticated position. AI is envisioned as a Presidents advisor, or a Fortune 500 company’s CEO’s assistant, or the wingman to a military pilot. In some ways today, this much is already true. Apple’s Siri, Microsoft’s Cortana, and Facebook’s M offer artificially intelligent assistance. (Although, M is indeed reported to be powered by real people.) Matured firms and infantile startups currently race to develop the best AI algorithm to tackle the stock market, to subtract mere milliseconds from trading times and increase the efficiency of these micro-transactions. The technology is already in action. According to MIT Technology Review, quantitative hedge funds like Bridgewater Associates, Renaissance Technologies, and D.E. Shaw already apply AI methods to manipulate the market.

Advancements in AI interest and technology over the past few years have seen machine intelligence move from the passenger seat to behind the wheel. The biggest tech companies around the world have invested in deep learning algorithms to distinguish patterns in huge amounts of data, teaching machines to learn with similar – though not equal – ability to you and I. Applying this method to finance has thus had something of a rebirth. At the Neural Information Processing Systems (NIPS) in Montreal in December, nearly half of the recruitment stands represented hedge funds and financial firms.

But AI sophistication isn’t strictly for the free market capitalists among us – it’ll soon be used to help the loveless as well. At least according to Bernie AI, a mobile dating technology that wants to help connect soul mates with one another. (No, there’s not relation to the presidential candidate.) Like Siri, Cortana, and M, Bernie AI is billed as an assistant, though not the type to remind you about your 4 o’clock conference call. Instead, Bernie AI uses intelligent algorithms to serve as a matchmaker. On their website, Bernie AI claims, “Bernie uses the latests advancements in machine learning and natural language processing to learn your ‘type’ of partner, and ensures the interest is mutual.”

The idea behind Bernie AI superficially determining love interest may not seem that far from what we do now. Even the manual processes of swiping right and left become something of a habit, rote flicks of the finger, for those who often use dating apps like Tinder and Grindr. But where Bernie AI goes one step further, is in engaging conversation with the matched interest to really weed out any potential disinterest. In a piece written for The New York Times BernieAI creator Justin Long notes how he’s programmed the system to “automate selection and basic introductory conversations – essentially, sorting through potential matches for the people who were genuinely interested in getting to know me.” So, Long applied facial recognition software to pinpoint and select facial features he finds attractive. If a match were met, the program would then send a message, expressing interest. If — and only if — the other party responded, Long would then take the reigns and commence the conversation.

Long insists he’s met some wonderful people through this system – people he’d presumably not have met otherwise, or would’ve spent ages searching for – but he has yet to find “the one”. And with this success, he decided to put his software into a beta version to help others explore the love world around them in a hands off but are efficient way.

Perhaps it’s this hands off feature that has many people so unsure about AI. Job automation – the buzz word that’s become one of the not-so-distant phobias for the youth of the world – is the ultimate manifestation of “hands off”. Meanwhile, less relieved and slightly more mediated AI involvement in finance and the dating world are slowly and steadily becoming a reality.

On Monday, The White House announced plans to co-host four upcoming public workshops on various AI topics to "spur public dialogue on artificial intelligence and machine learning and identify challenges and opportunities related to this emerging technology." Spearheaded by the Office of Science and Technology Policy, the workshops will be rolled out over the next few months (May to July) and will cover topics including implications in law and government, as well as the social and economic impacts. Workshop co-hosts include academic and non-profit institutions, as well as the National Economic Council. In addition, a new National Science and Technology Council (NSTC) subcommittee on machine learning and artificial intelligence will meet for the first time next week. The NSTC is currently working to leverage AI and machine learning technology in a variety of government services.

In case we haven’t been cautioned enough about the threats of emerging artificial intelligence, a panel of academics addressed the American Association for the Advancement of Science (AAAS) on Sunday with a warning that advancements in intelligent and semi-intelligent automation could lead to overwhelming unemployment across many industries.

Machines’ ability to recognize patterns is yet to match our own, but their increasing sophistication in regards to tasks like speech recognition and data analysis has seen AI applied to real world applications such as autonomous driving. In this vain Bart Selman, professor of computer science at Cornell University, said, “For the first time, we’re going to see these machines and systems as part of our everyday life.” The predicted success of self-driving cars may prove to be a blessing that greatly reduces car accidents, but – with 10% of U.S. jobs requiring some degree of vehicle operation – the technology will also undoubtedly effect the labor market. Moshe Vardi, professor of computer science and director of the Ken Kennedy Institute for Information Technology at Rice University, told AAAS, “We can expect the majority of these jobs will simply disappear.” He went on to suggest that the disconnect between the manufacturing industry and job growth is a result of automation. Though manufacturing volume is right now at its peak, U.S. manufacturing jobs are currently below the figures from the 1950s. He pointed to the 250,000 industrial robots in the U.S. and the increasing growth rate of their use. What Vardi suggests will happen is “job polarization”, a phenomenon that emerges when high-skilled jobs demand complex human intelligence and low-skilled jobs are too expensive to automate. Thus, the middle ground jobs will be the easiest to automate, leading to greater economic inequality. Vardi also noted that although this issue is widely regarded as a threat that could make a huge impact on American economic life, there is no discussion of it in politics, particularly not in the presidential election. “We need to start thinking very seriously: What will humans do when machines can do almost everything?” he said. “We have to redefine the meaning of good life without work.” Furthermore, Wendell Wallach, an ethicist at Yale University’s Interdisciplinary Center for Bioethics and the Hastings Center, said “There’s a need for concerted actions to keep technology a good servant and not let it become a dangerous matter.” He also proposed that 10% of AI research funding should be put towards studying the impact that AI machines will have on society, echoing Vardi’s concern that politics has failed to address the tremendous issue. “We need strong, meaningful human control,” he said.

A few weeks ago, Chinese software company Baidu released key parts of a key artificial intelligence/ speech recognition algorithm into the realm of open source, following in the footsteps of Facebook and Google last year.

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